Contents About Avid Nano Dynamic Light Scattering Products - - PowerPoint PPT Presentation

contents about avid nano dynamic light scattering
SMART_READER_LITE
LIVE PREVIEW

Contents About Avid Nano Dynamic Light Scattering Products - - PowerPoint PPT Presentation

Contents About Avid Nano Dynamic Light Scattering Products Accessories About Avid Nano Established in July 2009 Based in High Wycombe, UK Design & manufacture novel DLS DYNAMIC LIGHT SCATTERING Dynamic Light


slide-1
SLIDE 1
slide-2
SLIDE 2

Contents

 About Avid Nano  Dynamic Light Scattering  Products  Accessories

slide-3
SLIDE 3

About Avid Nano

 Established in July 2009  Based in High Wycombe, UK  Design & manufacture novel DLS

slide-4
SLIDE 4

DYNAMIC LIGHT SCATTERING

slide-5
SLIDE 5

Dynamic Light Scattering

 Observe time dependent intensity fluctuations

  • f light to directly measure...

– Hydrodynamic radius of molecules/particles in solution / suspension

  • Intensity size distribution
  • Mass size distribution
  • Aggregation
  • Molecular weight can be estimated
slide-6
SLIDE 6

Let's describe this visually

Scattering Volume

Ø40µm

slide-7
SLIDE 7

I

time Scattering Volume

slide-8
SLIDE 8

I

time Scattering Volume

slide-9
SLIDE 9

I

time Scattering Volume

slide-10
SLIDE 10

I

time Scattering Volume

slide-11
SLIDE 11

I

time Scattering Volume

slide-12
SLIDE 12

I

time

 Looks like random

noise, but...

 ...small sizes diffuse

more quickly than large sizes, so...

 ...rate of change tells

us the mean particle size

slide-13
SLIDE 13

Log10 time (µs) Amplitude, G2

 Intensity pattern

produces a correlation function and diffusion constant, Dt.

 Calculate mean

hydrodynamic radius, (Rh) and polydispersity index (PdI) Typical measurement time, 30s

Dynamic Light Scattering

Correlation function gives diffusion constant, Dt

slide-14
SLIDE 14

Dynamic Light Scattering

Rh= KT 6 Dt

We can use the 'Stokes- Einstein' equation to easily calculate the average hydrodynamic radius, Rh

slide-15
SLIDE 15

Size Distributions

0.01 0.1 1 10 100 1000 0.2 0.4 0.6 0.8 1 1.2

By comparing the measured data to a series of artificial correlation data we can produce a size distribution

Measured data Artificial data Size (nm) Intensity

slide-16
SLIDE 16

1.0 10.0 1.0 10.0 100.0 1000.0

f(x) = 2.75 x^2.49

Molecular Weight Estimator

Common Gobular Proteins

Monomer Radius (nm) MW (kDa)

To estimate molecular weight, we use a globular model derived from a curve

  • f common proteins

Molecular Weight Model

2.75r

2.49

Mw ~

slide-17
SLIDE 17

The model works well for many proteins

Molecular Weight Model

Molecule Size (nm)

  • Est. MW

(kDa) MW (kDa) Insulin (pH 2) 1.4 5.8 5.8 Lysozyme 2.0 14.5 14.7 Insulin (pH 7) 2.7 32.6 34.2 BSA 3.6 67.0 66.8 Hexokinase 4.3 104 102

slide-18
SLIDE 18

Typical Results

Experiment name Intensity Distribution Distribution Table of Results Mass Distribution

  • Ave. Correlation

Function Mean Size and Polydispersity

slide-19
SLIDE 19

Example Data

Highly monodisperse protein solution producing good quality crystals Narrow intensity peak Low polydispersity index Strong light scattering intensity from high concentration sample Mean radius 3.1nm used for molecular weight estimate (42kDa actual)

Mean radius Mw Estimate

slide-20
SLIDE 20

Example Data

Mixture of certified standards High PdI indicates broad or multi- mode distribution Intensity peaks confirm bi-modal distribution. Light scatters proportionally to Rh^6 Mass distribution indicates the amount of 10nm material much greater than 100nm

Mean radius. High Pd. Index

slide-21
SLIDE 21

Example Data

Overlaid Distributions Examples of mono-modal and multi-modal data. In each example we see normal variability caused by scattering intensity variation The size variability very low in main protein peak (7.1nm)

slide-22
SLIDE 22

Dynamic Light Scattering

 Primary data

– Mean hydrodynamic radius – Polydispersity index

 Secondary Data

– Intensity size distribution – Mass size distribution – Molecular weight estimate

slide-23
SLIDE 23

Applications

 Protein purification  Aggregation  Quick molecular weight estimate  Micelle formation  Thermal denaturing  Colloids and nano-particles

slide-24
SLIDE 24

Key Strengths

 Speed – especially with disposable cuvettes  Incredible sensitivity to aggregation  Requires little a priori knowledge  Absolute measurement - no calibration  Maintenance free

slide-25
SLIDE 25

Products

slide-26
SLIDE 26

W130i

 Designed for the protein specialist.

– Unbeatable sensitivity (0.1mg/ml, 15kDa protein) – 5µl disposable cuvette (standard) – Temperature control (0-90°C) – Compatible with standard cuvettes

slide-27
SLIDE 27

BladeCell Disposable Cuvette

slide-28
SLIDE 28

BladeCell Disposable Cuvette

DLS has reputation for being very useful but a bit tedious at times

– Expensive quartz cuvettes – Cleaning required – Cross-contamination issues

slide-29
SLIDE 29

BladeCell Disposable Cuvette

DLS has reputation for being very useful but a bit tedious at times

– Expensive quartz cuvettes – Cleaning required – Cross-contamination issues

BladeCell cuvette solves problem

– Only 5µl – No cleaning or reference m'ments – much faster than quartz – Full sample recovery – Zero cross-contamination

slide-30
SLIDE 30

Future Developments

slide-31
SLIDE 31

SMART-NANO

Commercializing hyper-sensitive DLS based on known light scattering techniques. Up to 10x sensitivity increase over current DLS performance. Suited to measurements at very low concentrations and for very small molecules Final negotiations September 2011. 2 year project commencing end 2011.

Sensitive MeAsuRemenT, detection, and identification of engineered NANO particles in complex matrices

slide-32
SLIDE 32

Thank you for watching